Landscape genomics training (AUS)

Pop. and Landscape genomics workshop, Canberra 2014


Session notes and slides :

Course (.pdf document)

Course – Slides (.pdf document)

Practical work (.pdf document)

The practical work on landscape genomics uses data on Loblolly pine (Pinus taeda) sampled in the US by the Eckert lab, (Eckert et al., 2010; Eckert et al., 2010). The purpose is to compute association models between SNPs data and environmental variables that will be downloaded or computed in a GIS.

We will use a GIS software mostly to visualize data (Quantum GIS), another one to produce environmental variables from Digital Elevation Models (DEMs) (SAGA GIS), and a third one to compute spatial statistics (OpenGeoda).

Quantum GIS can be found here:


SAGA GIS can be found here:

OpenGeoda can be found here:

Landscape Genomics software  

We will use are SamBada – based on multivariate logistic regressions -, LFMM, which considers population structure, and Admixture which computes membership coefficients to populations for each individual.


We will also use R for statistical analyses. You can install a modified GUI for R such as R studio

Following packages should be installed as well:



Genetic Data

We transformed genetic data to PLINK format in both binary (BED) and ordinary (PED) format.

Data can be found here:

Environmental Data

Sampling locations with aridity variables can be found here:

Several individuals have identical coordinates. In the purpose of visualizing them all in a GIS, we suggest modified coordinates.

Examples for Spatial Autocorrelation

We will use climatic variables from Worldclim datasets.

In the interest of time, we have created a subset for our study zone that you can download from our server :

Original datasets can be downloaded either by thiles or for the entire world:

DEMs can be found on Earth Explorer (subscription is mandatory before download):

If you dont want to sign up on EarthExplorer, you can download the DEM here


Recommended readings

Papers regarding practical work datasets can be found on Eckert’s blog:

We also recommend reading papers and documentation related to the software we will use